Data Preprocessing Pertemuan 03 Matakuliah: M0614 / Data Mining & OLAP Tahun : Feb

Bina Nusantara Pada akhir pertemuan ini, diharapkan mahasiswa akan mampu : Mahasiswa dapat menjelaskan karakteristik umum data. (C2) Mahasiswa dapat menerapkan data preprocessing: Data cleaning, data integration and transformation, dan data reduction yang hasilnya digunakan untuk analisis pada data mining. (C3) Learning Outcomes 3

Bina Nusantara Acknowledgments These slides have been adapted from Han, J., Kamber, M., & Pei, Y. Data Mining: Concepts and Technique.

Bina Nusantara General data characteristics Why pre-process the data? Basic data description and exploration Data cleaning Data integration and transformation Data reduction Summary Outline Materi 5

What is Data? Collection of data objects and their attributes An attribute is a property or characteristic of an object –Examples: eye color of a person, temperature, etc. –Attribute is also known as variable, field, characteristic, or feature A collection of attributes describe an object –Object is also known as record, point, case, sample, entity, or instance Attributes Objects

Types of Attribute Values Nominal –E.g., profession, ID numbers, eye color, zip codes Ordinal –E.g., rankings (e.g., army, professions), grades, height in {tall, medium, short} Binary –E.g., medical test (positive vs. negative) Interval –E.g., calendar dates, body temperatures Ratio –E.g., temperature in Kelvin, length, time, counts

Discrete and Continuous Attributes Discrete Attribute –Has only a finite or countably infinite set of values –Examples: zip codes, counts, or the set of words in a collection of documents –Often represented as integer variables. –Note: binary attributes are a special case of discrete attributes Continuous Attribute –Has real numbers as attribute values –Examples: temperature, height, or weight. –Practically, real values can only be measured and represented using a finite number of digits. –Continuous attributes are typically represented as floating-point variables.

Types of Data Sets Record – Relational records – Data matrix, e.g., numerical matrix, crosstabs – Document data: text documents: term- frequency vector – Transaction data Graph – World Wide Web – Social or information networks – Molecular Structures Ordered – Spatial data: maps – Temporal data: time-series – Sequential Data: transaction sequences – Genetic sequence data

Why pre-process the data? - Data Quality - What kinds of data quality problems? How can we detect problems with the data? What can we do about these problems? Examples of data quality problems: –Noise and outliers –missing values –duplicate data

Major Tasks in Data Preprocessing Data cleaning –Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies Data integration –Integration of multiple databases, data cubes, or files Data transformation –Normalization and aggregation Data reduction –Obtains reduced representation in volume but produces the same or similar analytical results –Data discretization: part of data reduction, of particular importance for numerical data

Data Cleaning No quality data, no quality mining results! –Quality decisions must be based on quality data e.g., duplicate or missing data may cause incorrect or even misleading statistics –“Data cleaning is the number one problem in data warehousing”— DCI survey –Data extraction, cleaning, and transformation comprises the majority of the work of building a data warehouse Data cleaning tasks –Fill in missing values –Identify outliers and smooth out noisy data –Correct inconsistent data –Resolve redundancy caused by data integration

Data in the Real World Is Dirty incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data –e.g., occupation=“ ” (missing data) noisy: containing noise, errors, or outliers –e.g., Salary=“−10” (an error) inconsistent: containing discrepancies in codes or names, e.g., –Age=“42” Birthday=“03/07/1997” –Was rating “1,2,3”, now rating “A, B, C” –discrepancy between duplicate records

Why Is Data Dirty? Incomplete data may come from –“Not applicable” data value when collected –Different considerations between the time when the data was collected and when it is analyzed. –Human/hardware/software problems Noisy data (incorrect values) may come from –Faulty data collection instruments –Human or computer error at data entry –Errors in data transmission Inconsistent data may come from –Different data sources –Functional dependency violation (e.g., modify some linked data) Duplicate records also need data cleaning

Multi-Dimensional Measure of Data Quality A well-accepted multidimensional view: –Accuracy –Completeness –Consistency –Timeliness –Believability –Value added –Interpretability –Accessibility Broad categories: –Intrinsic, contextual, representational, and accessibility

Missing Data Data is not always available –E.g., many tuples have no recorded value for several attributes, such as customer income in sales data Missing data may be due to –equipment malfunction –inconsistent with other recorded data and thus deleted –data not entered due to misunderstanding –certain data may not be considered important at the time of entry –not register history or changes of the data Missing data may need to be inferred

How to Handle Missing Data? Ignore the tuple: usually done when class label is missing (when doing classification)—not effective when the % of missing values per attribute varies considerably Fill in the missing value manually: tedious + infeasible? Fill in it automatically with –a global constant : e.g., “unknown”, a new class?! –the attribute mean –the attribute mean for all samples belonging to the same class: smarter –the most probable value: inference-based such as Bayesian formula or decision tree

Noisy Data Noise: random error or variance in a measured variable Incorrect attribute values may due to –faulty data collection instruments –data entry problems –data transmission problems –technology limitation –inconsistency in naming convention Other data problems which requires data cleaning –duplicate records –incomplete data –inconsistent data

How to Handle Noisy Data? Binning –first sort data and partition into (equal-frequency) bins –then one can smooth by bin means, smooth by bin median, smooth by bin boundaries, etc. Regression –smooth by fitting the data into regression functions Clustering –detect and remove outliers Combined computer and human inspection –detect suspicious values and check by human (e.g., deal with possible outliers)

Simple Discretization Methods: Binning Equal-width (distance) partitioning –Divides the range into N intervals of equal size: uniform grid –if A and B are the lowest and highest values of the attribute, the width of intervals will be: W = (B –A)/N. –The most straightforward, but outliers may dominate presentation –Skewed data is not handled well Equal-depth (frequency) partitioning –Divides the range into N intervals, each containing approximately same number of samples –Good data scaling –Managing categorical attributes can be tricky

Binning Methods for Data Smoothing  Sorted data for price (in dollars): 4, 8, 9, 15, 21, 21, 24, 25, 26, 28, 29, 34 * Partition into equal-frequency (equi-depth) bins: - Bin 1: 4, 8, 9, 15 - Bin 2: 21, 21, 24, 25 - Bin 3: 26, 28, 29, 34 * Smoothing by bin means: - Bin 1: 9, 9, 9, 9 - Bin 2: 23, 23, 23, 23 - Bin 3: 29, 29, 29, 29 * Smoothing by bin boundaries: - Bin 1: 4, 4, 4, 15 - Bin 2: 21, 21, 25, 25 - Bin 3: 26, 26, 26, 34

Regression x y y = x + 1 X1 Y1 Y1’

Cluster Analysis

Data Cleaning as a Process Data discrepancy detection –Use metadata (e.g., domain, range, dependency, distribution) –Check field overloading –Check uniqueness rule, consecutive rule and null rule –Use commercial tools Data scrubbing: use simple domain knowledge (e.g., postal code, spell-check) to detect errors and make corrections Data auditing: by analyzing data to discover rules and relationship to detect violators (e.g., correlation and clustering to find outliers) Data migration and integration –Data migration tools: allow transformations to be specified –ETL (Extraction/Transformation/Loading) tools: allow users to specify transformations through a graphical user interface Integration of the two processes –Iterative and interactive (e.g., Potter’s Wheels)

Data Integration Data integration: –Combines data from multiple sources into a coherent store Schema integration: e.g., A.cust-id  B.cust-# –Integrate metadata from different sources Entity identification problem: –Identify real world entities from multiple data sources, e.g., Bill Clinton = William Clinton Detecting and resolving data value conflicts –For the same real world entity, attribute values from different sources are different –Possible reasons: different representations, different scales, e.g., metric vs. British units

Handling Redundancy in Data Integration Redundant data occur often when integration of multiple databases –Object identification: The same attribute or object may have different names in different databases –Derivable data: One attribute may be a “derived” attribute in another table, e.g., annual revenue Redundant attributes may be able to be detected by correlation analysis Careful integration of the data from multiple sources may help reduce/avoid redundancies and inconsistencies and improve mining speed and quality

Correlation Analysis (Numerical Data) Correlation coefficient (also called Pearson’s product moment coefficient) –where n is the number of tuples, and are the respective means of p and q, σp and σq are the respective standard deviation of p and q, and Σ(pq) is the sum of the pq cross-product. If rp,q > 0, p and q are positively correlated (p’s values increase as q’s). The higher, the stronger correlation. rp,q = 0: independent; rpq < 0: negatively correlated

Correlation Analysis (Categorical Data) Χ 2 (chi-square) test The larger the Χ 2 value, the more likely the variables are related The cells that contribute the most to the Χ 2 value are those whose actual count is very different from the expected count Correlation does not imply causality –# of hospitals and # of car-theft in a city are correlated –Both are causally linked to the third variable: population

Chi-Square Calculation: An Example Χ 2 (chi-square) calculation (numbers in parenthesis are expected counts calculated based on the data distribution in the two categories) It shows that like_science_fiction and play_chess are correlated in the group Play chessNot play chessSum (row) Like science fiction250(90)200(360)450 Not like science fiction50(210)1000(840)1050 Sum(col.)

Data Transformation A function that maps the entire set of values of a given attribute to a new set of replacement values set each old value can be identified with one of the new values Methods –Smoothing: Remove noise from data –Aggregation: Summarization, data cube construction –Generalization: Concept hierarchy climbing –Normalization: Scaled to fall within a small, specified range min-max normalization z-score normalization normalization by decimal scaling –Attribute/feature construction New attributes constructed from the given ones

Data Transformation: Normalization Min-max normalization: to [new_min A, new_max A ] –Ex. Let income range $12,000 to $98,000 normalized to [0.0, 1.0]. Then $73,000 is mapped to Z-score normalization (μ: mean, σ: standard deviation): –Ex. Let μ = 54,000, σ = 16,000. Then Normalization by decimal scaling Where j is the smallest integer such that Max(|ν’|) < 1

Data Reduction Strategies Why data reduction? –A database/data warehouse may store terabytes of data –Complex data analysis/mining may take a very long time to run on the complete data set Data reduction: Obtain a reduced representation of the data set that is much smaller in volume but yet produce the same (or almost the same) analytical results Data reduction strategies –Dimensionality reduction — e.g., remove unimportant attributes –Numerosity reduction (some simply call it: Data Reduction) Data cub aggregation Data compression Regression Discretization (and concept hierarchy generation)

Data Cube Aggregation The lowest level of a data cube (base cuboid) –The aggregated data for an individual entity of interest –E.g., a customer in a phone calling data warehouse Multiple levels of aggregation in data cubes –Further reduce the size of data to deal with Reference appropriate levels –Use the smallest representation which is enough to solve the task Queries regarding aggregated information should be answered using data cube, when possible

Aggregation Combining two or more attributes (or objects) into a single attribute (or object) Purpose –Data reduction Reduce the number of attributes or objects –Change of scale Cities aggregated into regions, states, countries, etc –More “stable” data Aggregated data tends to have less variability

Data Compression String compression –There are extensive theories and well-tuned algorithms –Typically lossless –But only limited manipulation is possible without expansion Audio/video compression –Typically lossy compression, with progressive refinement –Sometimes small fragments of signal can be reconstructed without reconstructing the whole Time sequence is not audio –Typically short and vary slowly with time

Data Compression Original Data Compressed Data lossless Original Data Approximated lossy

Data Reduction: Histograms Divide data into buckets and store average (sum) for each bucket Partitioning rules: –Equal-width: equal bucket range –Equal-frequency (or equal-depth) –V-optimal: with the least histogram variance (weighted sum of the original values that each bucket represents) –MaxDiff: set bucket boundary between each pair for pairs have the β – 1 largest differences

Data Reduction Method: Clustering Partition data set into clusters based on similarity, and store cluster representation (e.g., centroid and diameter) only Can be very effective if data is clustered but not if data is “smeared” Can have hierarchical clustering and be stored in multi-dimensional index tree structures There are many choices of clustering definitions and clustering algorithms

Data Reduction Method: Sampling Sampling: obtaining a small sample s to represent the whole data set N Allow a mining algorithm to run in complexity that is potentially sub-linear to the size of the data Key principle: Choose a representative subset of the data –Simple random sampling may have very poor performance in the presence of skew –Develop adaptive sampling methods, e.g., stratified sampling: Note: Sampling may not reduce database I/Os (page at a time)

Types of Sampling Simple random sampling –There is an equal probability of selecting any particular item Sampling without replacement –Once an object is selected, it is removed from the population Sampling with replacement –A selected object is not removed from the population Stratified sampling: –Partition the data set, and draw samples from each partition (proportionally, i.e., approximately the same percentage of the data) –Used in conjunction with skewed data

Sampling: With or without Replacement

Sampling: Cluster or Stratified Sampling

Data Reduction: Discretization Three types of attributes: –Nominal — values from an unordered set, e.g., color, profession –Ordinal — values from an ordered set, e.g., military or academic rank –Continuous — real numbers, e.g., integer or real numbers Discretization: –Divide the range of a continuous attribute into intervals –Some classification algorithms only accept categorical attributes. –Reduce data size by discretization –Prepare for further analysis

Discretization and Concept Hierarchy Generation for Numeric Data Typical methods: All the methods can be applied recursively –Binning (covered above) Top-down split, unsupervised, –Histogram analysis (covered above) Top-down split, unsupervised –Clustering analysis (covered above) Either top-down split or bottom-up merge, unsupervised

Concept Hierarchy Generation for Categorical Data Specification of a partial/total ordering of attributes explicitly at the schema level by users or experts –street < city < state < country Specification of a hierarchy for a set of values by explicit data grouping –{Urbana, Champaign, Chicago} < Illinois Specification of only a partial set of attributes –E.g., only street < city, not others Automatic generation of hierarchies (or attribute levels) by the analysis of the number of distinct values –E.g., for a set of attributes: {street, city, state, country}

Automatic Concept Hierarchy Generation Some hierarchies can be automatically generated based on the analysis of the number of distinct values per attribute in the data set –The attribute with the most distinct values is placed at the lowest level of the hierarchy –Exceptions, e.g., weekday, month, quarter, year country province_or_ state city street 15 distinct values 365 distinct values 3567 distinct values 674,339 distinct values

Summary Data preparation/preprocessing: A big issue for data mining Data description, data exploration, and measure data similarity set the base for quality data preprocessing Data preparation includes –Data cleaning –Data integration and data transformation –Data reduction (dimensionality and numerosity reduction) A lot a methods have been developed but data preprocessing still an active area of research

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